Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

TL;DR

This paper introduces a zero-bias deep learning framework for IoT device identification using physical signals, enhancing robustness and interpretability, and capable of recognizing unseen devices without cryptographic keys.

Contribution

It presents a novel zero-bias neural network approach for non-cryptographic IoT device identification, improving robustness and interpretability over existing methods.

Findings

Effective identification of IoT devices using physical layer signals

Ability to recognize unseen IoT devices

Enhanced robustness and interpretability of the model

Abstract

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT make it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Non-cryptographic device verification is needed to ensure trustworthy IoT. In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Codes and data are available in IEEE Dataport.